José G. Dias

According to our database, José G. Dias authored at least 15 papers between 2004 and 2021.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2021

MAINT.Data: Modelling and Analysing Interval Data in R.

António Pedro Duarte Silva, Paula Brito, Peter Filzmoser, José G. Dias

R J., 2021

2017

Extracting clusters from aggregate panel data: A market segmentation study.

Graça Trindade, José G. Dias, Jorge A. Ambrósio

Appl. Math. Comput., 2017

2015

Probabilistic clustering of interval data.

Paula Brito, António Pedro Duarte Silva, José G. Dias

Intell. Data Anal., 2015

Survival mixture models in behavioral scoring.

Bruno Cardoso Alves, José G. Dias

Expert Syst. Appl., 2015

Clustering financial time series: New insights from an extended hidden Markov model.

José G. Dias, Jeroen K. Vermunt, Sofia Ramos

Eur. J. Oper. Res., 2015

2014

Dynamic clustering of energy markets: An extended hidden Markov approach.

José G. Dias, Sofia Ramos

Expert Syst. Appl., 2014

Mining categorical sequences from data using a hybrid clustering method.

Luca De Angelis, José G. Dias

Eur. J. Oper. Res., 2014

2013

Model-Based Clustering of Multistate Data with Latent Change: An Application with DHS Data.

José G. Dias

Proceedings of the Classification and Data Mining, 2013

Latent Class Models of Time Series Data: An Entropic-Based Uncertainty Measure.

José G. Dias

Proceedings of the Algorithms from and for Nature and Life, 2013

2008

A bootstrap-based aggregate classifier for model-based clustering.

José G. Dias, Jeroen K. Vermunt

Comput. Stat., 2008

The SKM Algorithm: A K-Means Algorithm for Clustering Sequential Data.

José G. Dias, Maria João Cortinhal

Proceedings of the Advances in Artificial Intelligence, 2008

Mixture Hidden Markov Models in Finance Research.

José G. Dias, Jeroen K. Vermunt, Sofia Ramos

Proceedings of the Advances in Data Analysis, Data Handling and Business Intelligence, 2008

2006

Model Selection Criteria for Model-Based Clustering of Categorical Time Series Data: A Monte Carlo Study.

José G. Dias

Proceedings of the Advances in Data Analysis, 2006

2004

An empirical comparison of EM, SEM and MCMC performance for problematic Gaussian mixture likelihoods.

José G. Dias, Michel Wedel

Stat. Comput., 2004

Bootstrapping Latent Class Models.

José G. Dias

Proceedings of the Classification, 2004